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Costory: Your Finops MCP

list_metrics

Read-only

List saved Costory business metrics and, optionally, matching live external metrics from connected integrations (e.g. Tsuga, BigQuery, Datadog, CloudWatch, Google Cloud Monitoring). Saved metrics return id/name/type for { type: "metric", metricId: "..." } in query. Set includeExternal: true with a specific search term to return externalMetrics with provider, integrationId, integrationName, metricName, unit, capabilities, and attributes. That catalog is enough to build { type: "externalMetric", provider, integrationId, metricName, aggregator, groupByFields, conditions } for Tsuga, Datadog (attributes are tag keys; metricName is the Datadog metric name), CloudWatch (metricName is Namespace/MetricName such as AWS/EC2/CPUUtilization; attributes are dimension names), Google Cloud Monitoring (provider "cloudmonitoring"; metricName is the metric type such as compute.googleapis.com/instance/cpu/utilization; attributes are fully-qualified group-by fields such as metric.label.state or resource.label.project_id, and conditions is an optional Monitoring filter), or Amplitude. For BigQuery or S3, the same hit also includes schema (typed columns: DATE / NUMERIC / STRING). Pick dateColumn from the first DATE field, metricColumn from the first NUMERIC field, and groupByFields from STRING fields (also listed in attributes). gapFillingMethod is not in the catalog — default FORWARD_FILL unless the user specifies ZERO, LINEAR_INTERPOLATION, or SPREAD. Those three fields are required on both query and a new virtual-dimension telemetry allocation. Do not call includeExternal without search; external catalogs can be large, and the tool will ask for a search term instead of listing everything. externalLimit (default 50, max 50) caps matching external results. New virtual-dimension telemetry reallocations use includeExternal + an inline externalMetric (same fields as a query series, plus exactly one groupByFields entry as the series key). Pass datasourceId only to inspect a leftover saved usage-metric datasource's groupByDimensions — not for new reallocations. Then call query (type: "metric", metricId, groupBy) to inspect the values for that leftover datasource. The datasourceId is the same metricsDatasource id this tool returns as a saved-metric id (strip any ::metricName suffix). Does not return values (use query). EXAMPLES: • "What business metrics do we have?" → {} • "Find Tsuga metrics about requests" → { includeExternal: true, search: "request" } • "Find BigQuery tables about revenue" → { includeExternal: true, search: "revenue" } • "Find Datadog metrics about CPU" → { includeExternal: true, search: "cpu" } • "Find CloudWatch metrics about CPU" → { includeExternal: true, search: "CPUUtilization" } • "Find GCP Monitoring metrics about CPU" → { includeExternal: true, search: "cpu/utilization" } (Cloud Monitoring search matches the metric type, not its display name) • "What live metrics can I reallocate shared cost by?" → { includeExternal: true, search: "cpu" } • "What can I split a leftover saved-metric datasource by?" → { datasourceId: "clx…" }

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugNoOrganization slug. Omit to auto-detect from your account (fails if you belong to multiple orgs).
searchNoOptional filter on saved metric names. Required when includeExternal is true because external metric catalogs can be large.
datasourceIdNoSpecial list mode (not a query field): return groupBy dimensions for ONE leftover saved usage-metric datasource. New telemetry reallocations use includeExternal + inline externalMetric instead. Pass the metricsDatasource id from metrics[].id (strip any `::metricName` suffix). Then call query with type:"metric", metricId:<same id>, groupBy:<dimension>. Do not confuse with query metricId — that is the identity field on the query tool; this filter only returns dimensions. When set, search / includeExternal are ignored.
externalLimitNoMax matching external metrics to return when includeExternal is true and search is provided (capped at 50).
includeExternalNoWhen true and search is provided, include matching live integration-backed external metrics in externalMetrics.

TDQS

A4.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already cover read-only and non-destructive behavior, so the description does not need to re-establish safety. It adds useful behavioral context: external catalogs can be large and will ask for a search term, externalLimit is capped at 50, gapFillingMethod defaults to FORWARD_FILL, and the tool returns metadata only, not actual values.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is long but dense, and nearly every sentence carries provider-specific behavior an agent needs. It front-loads the core purpose, then details edge cases and ends with examples. It is not as compact as the best definitions, but it contains little filler and uses examples to illustrate the main parameter modes.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description carries the full burden of explaining return shapes, and it does: saved metric id/name/type, externalMetrics fields, BigQuery/S3 schema, datasourceId special mode, gapFilling default, and required fields for query or reallocations. For a tool with this many provider-specific modes, the coverage is comprehensive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Even though schema coverage is 100%, the description substantially enriches the parameters: search is required when includeExternal is true, datasourceId has a special list mode with exact id-suffix handling, includeExternal selects a specific return shape, and externalLimit has documented defaults and caps. It also explains how returned fields map to downstream query and externalMetric construction.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource: list saved Costory business metrics and optionally matching live external metrics. It clearly separates this tool from query by stating it does not return values, which helps an agent pick it correctly among the sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit usage rules: use includeExternal only with a search term, pass datasourceId only for inspecting leftover datasource groupByDimensions, and call query afterwards to inspect values. The examples map natural-language requests to concrete parameter objects, which is strong when-to-use guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.1/5.0
Disambiguation4/5

Tools are organized by resource (alerts, dashboards, reports, events, virtual dimensions) with distinct actions, so most are clearly separable. The main confusion risks are the three report-delivery side-effect tools (run_report_now, retry_report_execution, transfer_report_execution) and the generic get that spans five resource types, though detailed descriptions mitigate these.

Naming Consistency4/5

The dominant verb_noun pattern (create_*, list_*, update_*, preview_*, get_*) is consistent and predictable across the set. Deviations like bare verbs query/search/get and the noun-only virtual_dimension_overlap_matrix are readable but break the otherwise uniform convention.

Tool Count3/5

44 tools is heavy and exceeds the comfortable range, but the server covers a genuinely broad FinOps platform spanning querying, dashboards, reports, alerts, events, virtual dimensions, docs, skills, and suggestions. Each tool has a distinct job, though the sheer count makes agent navigation harder.

Completeness3/5

Core workflows are well covered: query → dashboard/report/alert/event, plus a full virtual-dimension draft lifecycle. Notable gaps include alerts being create-only with no update/delete, no deletes for dashboards/events/published virtual dimensions, and budget management limited to query/get with no create/update.

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